In the biopharmaceutical industry, preparative chromatography using packed-bed columns is a key component in the manufacture of complex biological products (e.g. recombinant proteins & antibodies). Accordingly, it is critical that chromatography column performance is closely monitored and well controlled to ensure high product quality. For example, a high column packing quality is required for efficient chromatographic operations, and deviations from an ideally packed column can result in sub-optimal performance, including increased mobile phase dispersion, poor protein separation, and, potentially, product rejection.
A critical aspect for successful preparative chromatography rests on the ability to implement the best possible methods of process monitoring. Monitoring of chromatography-based processes is typically focused on ensuring that the columns are performing per expectations. Common areas of concern in chromatography using packed-bed columns include, for example: (1) degradation in performance due to column integrity being compromised; (2) degradation due to columns approaching their lifetime limits; (3) equipment malfunction causing problems; and (4) column characteristics changing over time. As such, the function of process monitoring in chromatography processes is one of developing and implementing optimal systems for detecting and addressing inadequate performance or undesirable changes in chromatography procedures.
Today, chromatography process monitoring is performed using a number of methods, such as pulse-input based HETP (Height Equivalent to a Theoretical Plate), monitoring univariate parameters such as asymmetry of a chromatographic peak, elution UV peak width, product yields, and by performing qualitative visual checks of columns and chromatography profiles in attempt to identify anamolies therein.
However, while these methods are useful, they do not provide a sufficiently sensitive and encompassing means for detecting changes or degradation in column performance. Further, in the context of protein purification, it is necessary not only to detect the inherently stochastic behavior associated with the process but also to mitigate the impact of unexpected changes within the columns.
Another problem is that conventional techniques of calculating on-line pulse test HETP are more likely to result in “false positives” which means a packed chromatography column that should have been rejected is, instead, passed. Furthermore, conventional pulse test HETP does not typically reveal gradual trends in column performance. Consequently, by using conventional column chromatography monitoring techniques, valuable product may be run through bad columns and wasted. Conversely, a quality column (which has otherwise shown no adverse trends) may be unnecessarily re-packed when a one time pulse test HETP failure occurs.
Accordingly, there is a need for more quantitative, robust, and less time consuming methods and systems for monitoring and/or evaluating chromatography column performance.